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We used deep learning methods to create an innovative system for early detection of depression based on user comments on social networks. This revolutionary approach exploits large amounts of textual data available online to identify depression. The data set originates from the eRisk 2022 competition. Thanks to natural language and statistical modeling techniques, our system can analyze user comments and detect their depression. We applied deep learning methods, in particular the BiLSTM(Bidirectional Long Short-Term Memory) model. Using the approach, we obtained an F-score of 42.96%.

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Deep Learning Approach for Early Prediction of Depression on Social Network

  • 会议论文

  • 发表日期：

    2025年

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  • Manel Ben Amira,
  • Nabil Khoufi,
  • Chafik Aloulou

摘要

We used deep learning methods to create an innovative system for early detection of depression based on user comments on social networks. This revolutionary approach exploits large amounts of textual data available online to identify depression. The data set originates from the eRisk 2022 competition. Thanks to natural language and statistical modeling techniques, our system can analyze user comments and detect their depression. We applied deep learning methods, in particular the BiLSTM(Bidirectional Long Short-Term Memory) model. Using the approach, we obtained an F-score of 42.96%.